Eran Rubens

AI
Self-driving doctors? Auto industry offers model for AI use
Analogies can be drawn between the development of autonomous driving technology and artificial intelligence (AI) in medicine. In this column, Eran Rubens, chief technology officer for enterprise imaging at Philips Healthcare, believes that an auto industry model for classifying automation could be adapted to the practice of medicine.
July 24, 2017
2017 07 07 11 33 30 600 Cars Self Driving 400
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Mammogram Patient
Womens Imaging
Mammo uptake among veterans on par with nonveterans
Female veterans are as likely to undergo screening mammography as nonveteran women.
Patient examples of SPECT/CT and PET/CT RECIP 1.0. (A) SPECT and PET maximum-intensity projection (MIP) images in 74-year-old man with mCRPC. Serum PSA level at baseline was 449 ng/mL and slightly increased to 525 ng/mL after two cycles of Lu-177 PSMA. Eastern Cooperative Oncology Group (ECOG) score was 2 at baseline and increased to 3 after 2 cycles. The patient was classified by readers as RECIP-progressive disease on both SPECT and PET. Overall survival was 4.7 months. Two cycles of Lu-177 PSMA were administered. (B) SPECT and PET MIP images in 74-year-old man with mCRPC. Serum PSA level was 231 ng/mL at baseline and decreased to 12.5 ng/mL after 2 cycles of Lu-177 PSMA. ECOG score was less than 2 at baseline and after 2 cycles of Lu-177 PSMA. The patient was classified by readers as RECIP-partial response on both SPECT and PET. Overall survival was 14.3 months. Four cycles of Lu-177 PSMA were administered.
Molecular Imaging
SPECT/CT predicts overall survival in Pluvicto patients
Glioma Letters Brain
MRI
AI-assisted brain MRI shows promise for improved glioma care
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MRI
Deep-learning technique boosts diagnostic ability of fast knee MRI
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Molecular Imaging
PET scans identify people at early risk for cognitive decline
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Mammo uptake among veterans on par with nonveterans
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Female veterans are as likely to undergo screening mammography as nonveteran women.
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SPECT/CT predicts overall survival in Pluvicto patients
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New evidence supports using SPECT/CT scans along with serum PSA levels to evaluate early patient responses to Pluvicto treatment.
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Patient examples of SPECT/CT and PET/CT RECIP 1.0. (A) SPECT and PET maximum-intensity projection (MIP) images in 74-year-old man with mCRPC. Serum PSA level at baseline was 449 ng/mL and slightly increased to 525 ng/mL after two cycles of Lu-177 PSMA. Eastern Cooperative Oncology Group (ECOG) score was 2 at baseline and increased to 3 after 2 cycles. The patient was classified by readers as RECIP-progressive disease on both SPECT and PET. Overall survival was 4.7 months. Two cycles of Lu-177 PSMA were administered. (B) SPECT and PET MIP images in 74-year-old man with mCRPC. Serum PSA level was 231 ng/mL at baseline and decreased to 12.5 ng/mL after 2 cycles of Lu-177 PSMA. ECOG score was less than 2 at baseline and after 2 cycles of Lu-177 PSMA. The patient was classified by readers as RECIP-partial response on both SPECT and PET. Overall survival was 14.3 months. Four cycles of Lu-177 PSMA were administered.
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Glioma Letters Brain
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PET scans identify people at early risk for cognitive decline
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Amyloid PET scans in people with early signs of Alzheimer’s can identify those at higher risk of progressing to cognitive decline.
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Most veterans adhere to post-EVAR surveillance imaging
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Adherence to surveillance imaging after endovascular abdominal aortic aneurysm repair (EVAR) is high among veterans.
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Quantitative CT textures of the lung help assess COPD severity
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Lung POCUS helps diagnose dyspnea in emergency departments
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Point-of-care lung ultrasound may be useful in the emergency department for diagnosing dyspnea.
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Images show lung ultrasound findings of major pleuro-pulmonary pathologies. (a) Pleural effusion; (b) Pneumonia; (c) Bronchopneumonia; (d) Acute pulmonary edema; (e) Chronic obstructive pulmonary disease exacerbation; (f) Pleuropulmonary tumors; (g) Acute respiratory distress syndrome.
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Findings show that AI could potentially expand the utility of neuroimaging on portable MRI systems.
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Figure: Improved image quality with a generative adversarial network architecture for low- to high-field-strength image translation, called LowGAN. (A–C) Top: High-field-strength (3-T) and low-field-strength (64-mT) images and LowGAN outputs for a single participant across (A) fluid-attenuated inversion recovery (FLAIR), (B) T1-weighted (T1w), and (C) T2-weighted (T2w) contrasts. Bottom: Graphs demonstrate the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and feature similarity index (FSIM) between high field strength and low field strength (64-mT) and high field strength and LowGAN outputs. a.u. = arbitrary units.****p ≤ 0.0001. Images and caption courtesy of Radiology and the RSNA.
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